Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms
Abstract
This paper demonstrates how Genetic Algorithms can be used to discover the structure of a Bayesian Network from a given database with cases. The results presented, were obtained by applying four different types of Genetic Algorithms - SSGA (Steady State Genetic Algorithm), GAe $\lambda$ (Genetic Algorithm elistist of degree $\lambda$ ), hSSGA (hybrid Steady State Genetic Algorithm) and the hGAe $\lambda$ (hybrid Genetic Algorithm elitist of degree $\lambda$ ) - to simulations of the ALARM Network. The behaviour of the mentioned algorithms is studied with respect to their parameters.
Cite
Text
Larrañaga et al. "Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[Larrañaga et al. "Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/larranaga1995aistats-structure/)BibTeX
@inproceedings{larranaga1995aistats-structure,
title = {{Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms}},
author = {Larrañaga, Pedro and Murga, Roberto H. and Poza, Mikel and Kuijpers, Cindy M. H.},
booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
year = {1995},
pages = {310-316},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/larranaga1995aistats-structure/}
}